Assays based upon next generation sequencing technologies (*-seq assays) are widely used in the genomics community. As these assays mature and attempt to probe more subtle biological phenomenon, new tools based upon powerful statistical techniques will be needed to provide confidence in the resulting biological conclusions. To date, *-seq assay analysis tools can be split into two distinct classes, mapping and quantification. Mapping tools attempt to match each read with a genomic location, whereas quantification tools infer biological features from the mapped reads. The results of the mapping are often very dependent on tuning parameters and rarely, if ever, provide any notion of confidence. The analysis tools typically take the provided mappings as gospel. This project will take a different approach. The investigators propose to use known physical and biochemical properties of the assay to model the assay. Such an approach will yield better mappings, while providing a notion of confidence that can be made an integral part of downstream analysis. Extensive validation of the software and underlying models is planned in three organisms using data from five different validatory experiments. The work proposed in this project will result in significant improvements in the analyses of *-seq data. If successful, this project will replace a host of mapping algorithms, peak callers, and transcript quantifiers, forming the foundation of a software suite for the integrative analysis of *-seq assays.